CN109254991A - A kind of interactive learning methods and device - Google Patents
A kind of interactive learning methods and device Download PDFInfo
- Publication number
- CN109254991A CN109254991A CN201811234259.7A CN201811234259A CN109254991A CN 109254991 A CN109254991 A CN 109254991A CN 201811234259 A CN201811234259 A CN 201811234259A CN 109254991 A CN109254991 A CN 109254991A
- Authority
- CN
- China
- Prior art keywords
- user
- learning
- language
- data
- level
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
- 230000013016 learning Effects 0.000 title claims abstract description 178
- 238000000034 method Methods 0.000 title claims abstract description 31
- 230000002452 interceptive effect Effects 0.000 title claims abstract description 16
- 230000006399 behavior Effects 0.000 claims abstract description 27
- 230000003993 interaction Effects 0.000 claims abstract description 21
- 230000003044 adaptive effect Effects 0.000 claims abstract description 18
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 10
- 238000012360 testing method Methods 0.000 claims description 19
- 238000012545 processing Methods 0.000 claims description 18
- 230000004048 modification Effects 0.000 claims description 7
- 238000012986 modification Methods 0.000 claims description 7
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 5
- 230000003542 behavioural effect Effects 0.000 claims 1
- 238000004458 analytical method Methods 0.000 abstract description 3
- 238000001514 detection method Methods 0.000 abstract description 3
- 230000000875 corresponding effect Effects 0.000 description 14
- 238000010586 diagram Methods 0.000 description 9
- 238000004590 computer program Methods 0.000 description 7
- 230000008569 process Effects 0.000 description 5
- 230000000694 effects Effects 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
- 238000011160 research Methods 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 238000010276 construction Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 238000003860 storage Methods 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000006731 degradation reaction Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 239000004744 fabric Substances 0.000 description 1
- 238000012812 general test Methods 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000008450 motivation Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000007474 system interaction Effects 0.000 description 1
- 230000009897 systematic effect Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
- 238000012800 visualization Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
- G06Q50/205—Education administration or guidance
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Educational Administration (AREA)
- Educational Technology (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Health & Medical Sciences (AREA)
- Economics (AREA)
- General Health & Medical Sciences (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Electrically Operated Instructional Devices (AREA)
Abstract
The present invention provides a kind of interactive learning methods and device, method includes: that the language learning interaction data of acquisition user determines the language proficiency of user;Wherein, the language proficiency data of the user include: the language initial level of user, current language level;Initial learning model is determined according to the language initial level of user;Initial learning model is updated using adaptive algorithm according to current language level, user carries out language learning according to updated learning model.The present invention provides advice language learner learning path, pass through the crawl to learner's practice data simultaneously, real-time judge learner's real-time learning progress, and pass through the analysis to learner's learning behavior, the recommendation of individual character is carried out in practice and study-leading, so that learner clearly holds self-teaching progress, leakage detection is carried out in time and is filled a vacancy, to improve learning efficiency.
Description
Technical field
The present invention relates to data processing techniques, are concretely a kind of interactive learning methods and device.
Background technique
The research emphasis of current teaching Chinese as a foreign language technology is in Resources Database and MOOC (massive open
Online courses, large-scale open network course) research aspect, emphasis is Contents Construction, passes through the convenient channel of network
Learn with multimedia ways of presentation computer-aided Chinese learning person, and in field research, mainly pass through the study of learner
Purpose classification carries out Classified Teaching, the form for being still group instruction of progress.
Contents Construction work provides the resource of magnanimity for Chinese studying person, but the resource of this generality and group
Teaching method still lacks the understanding to learner's customized information, can not accurately be when difficulty of learning occurs for learner
Reason is held, can not targetedly be instructed, is not all truly realized and teaches students in accordance with their aptitude in the content of courses and course arrangement,
Also real assisted teacher's teaching or learners' corpora can not be accomplished to the space of many Active Learnings of learner
It practises.
Summary of the invention
For the efficiency for improving language learning, the targeted content of courses is provided to different user, the embodiment of the present invention mentions
A kind of interactive learning methods are supplied, the method includes:
The language learning interaction data for obtaining user determines the language proficiency of user;Wherein, the language water of the user
Flat includes: language initial level, the current language level of user;
According to the language initial water of the user learning model initial according to determination;
The initial learning model, Yong Hugen are updated using adaptive algorithm according to the current language horizontal data
Language learning is carried out according to updated learning model.
In the embodiment of the present invention, the language learning interaction data includes: the essential information data of user's input, user
Learning behavior data.
In the embodiment of the present invention, the language learning interaction data of the acquisition user determines the language proficiency packet of user
It includes:
The language initial level of user is determined according to the essential information data that user inputs;Wherein, the essential information
Data include: age of user, country origin and current language hierarchy level;
Current language level is determined according to the learning behavior data for obtaining user;Wherein the learning behavior data include:
The practice data of user, learning Content data.
It is described to determine initial learning model according to the language initial level data of the user in the embodiment of the present invention
Include:
Corresponding user's initial level is selected from preset teaching database according to the language initial level data of user
Teaching data and practice data are as initial learning model.
It is described described initial using adaptive algorithm update according to the current language level in the embodiment of the present invention
Learning model include:
Corresponding weighted value is respectively set to the learning Content data of user, test data, practice data;
Determine that user's is current according to the learning Content data of user, test data, practice data and corresponding weighted value
Language proficiency;
The learning Content of corresponding user's present level is selected from preset teaching database according to current language proficiency
Data practice data as current learning model.
Meanwhile the present invention also provides a kind of language learning device, device includes:
Interaction data processing module, the language learning interaction data for obtaining user determine the language proficiency of user;Its
In, the language proficiency of the user includes: the language initial level of user, current language level;
Model building module, for determining initial learning model according to the language initial level of the user;
Model modification module, for updating initial using adaptive algorithm according to the current language level
Model is practised, user carries out language learning according to updated learning model.
In the embodiment of the present invention, the language learning interaction data includes: the essential information data of user's input, user
Learning behavior data.
In the embodiment of the present invention, the interaction data processing module includes:
Basic processing unit determines the language initial level of user according to the essential information data that user inputs;Its
In, the essential information data include: age of user, country origin and current language hierarchy level;
Learning data processing unit determines current language level according to the learning behavior data for obtaining user;It is wherein described
Learning behavior data include: the practice data of user, learning Content data.
In the embodiment of the present invention, model modification module includes:
Correspondence is respectively set for learning Content data, test data, the practice data to user in weight value setting unit
Weighted value;
Language proficiency determination unit, according to the learning Content data of user, test data, practice data and corresponding weight
It is worth and determines that the current language of user is horizontal;
Model modification unit, for being selected corresponding user to work as from preset teaching database according to current language proficiency
The learning Content data of preceding level, test data and practice data update initial learning model as current learning model.
In the embodiment of the present invention, the model building module, further according to the language initial level of user from default
Teaching database in select corresponding user's initial level teaching data and practice data as initial learning model.
The present invention provides advice language learner learning path, while the crawl by practicing learner data, in real time
Judge learner's real-time learning progress, and by the analysis to learner's learning behavior, is carried out in practice and study-leading
The recommendation of individual character carries out leakage detection in time and fills a vacancy so that learner clearly holds self-teaching progress, to improve study effect
Rate.
For above and other objects, features and advantages of the invention can be clearer and more comprehensible, preferred embodiment is cited below particularly,
And cooperate institute's accompanying drawings, it is described in detail below.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention for those of ordinary skill in the art without creative efforts, can be with
It obtains other drawings based on these drawings.
Fig. 1 is a kind of flow chart of interactive learning methods provided by the invention;
Fig. 2 is the block diagram of language learning device provided by the invention;
Fig. 3 is the block diagram of one embodiment of the invention;
Fig. 4 is the flow chart of interactive learning methods provided in an embodiment of the present invention;
Fig. 5 is the flow chart of the embodiment of the present invention;
Fig. 6 is the flow chart of the embodiment of the present invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
As shown in Figure 1, the present invention provides a kind of interactive learning methods, comprising:
Step S101, the language learning interaction data for obtaining user determine the language proficiency of user;Wherein, the user
Language proficiency data include: that the language initial level of user, current language are horizontal;
Step S103 determines initial learning model according to the language initial level of user;
Step S105 updates the initial learning model, Yong Hugen using adaptive algorithm according to current language level
Language learning is carried out according to updated learning model.
In the embodiment of the present invention, the language learning interaction data includes: the essential information data of user's input, user
Learning behavior data.
In the embodiment of the present invention, the language learning interaction data of the acquisition user determines the language proficiency data of user
Include:
The language initial level of user is determined according to the essential information data that user inputs;Wherein, the essential information
Data include: age of user, country origin and current language hierarchy level;
Current language horizontal data is determined according to the learning behavior data for obtaining user;The wherein learning behavior data packet
It includes: the practice data of user, learning Content data.
It is described to determine initial learning model according to the language initial level data of the user in the embodiment of the present invention
Include:
Corresponding user's initial level is selected from preset teaching database according to the language initial level data of user
Teaching data and practice data are as initial learning model.
Meanwhile as shown in Fig. 2, the present invention also provides a kind of language learning device, device includes:
Interaction data processing module 201, the language learning interaction data for obtaining user determine the language proficiency of user
Data;Wherein, the language proficiency data of the user include: the language initial level data of user, current language number of levels
According to;
Model building module 202, for determining initial learning model according to the language initial level data of user;
Model modification module 203, it is described initial for being updated according to current language horizontal data using adaptive algorithm
Learning model, user carry out language learning according to updated learning model.
In the embodiment of the present invention, interaction data processing module 201 includes:
Basic processing unit determines the language initial level number of user according to the essential information data that user inputs
According to;Essential information data include: age of user, country origin and current language hierarchy level;
Learning data processing unit determines current language horizontal data according to the learning behavior data for obtaining user;Wherein
The learning behavior data include: the practice data of user, learning Content data.
Technical solution of the present invention is described in further detail below with reference to specific embodiment:
The present embodiment discloses a kind of Chinese studying systems, by the system to above-mentioned interactive learning methods and device make into
One step is described in detail, as shown in figure 3, mainly having three big moulds for the structural block diagram of Chinese studying systems disclosed in the present embodiment
Block: resource layer, user model and recommended engine module, using presentation layer.Wherein resource layer (is based on dividing including the use of mongodb
Cloth file storage database) establish to knowledge of grammar library and grammar exercise exam pool (that is, preset religion above-mentioned
Learn database), in the present embodiment, comprising to Chinese and English explanation, difficulty of knowledge points and rank, example sentence, picture in knowledge of grammar library
Illustrate, the information such as video explanation, grammar exercise exam pool includes item difficulty, answer mark, topic types and correlated knowledge point etc.
Markup information obtains relevant information will pass through exercise mesh.
User model and recommended engine module include: personalized user model and adaptive learning recommended engine, are passed through
Adaptive recommended engine, the personalized model based on learner suggest and push to learner according to the language proficiency of user to learn
Practise resource;
It include: grammar learning, three exercise practice, study schedule parts using presentation layer, wherein language in the present embodiment
Calligraphy learning part is the presentation of knowledge point, learns for learner and participates in, and exercise practice part is divided into specific exercises and random again
Practice.Specific exercises are the practices carried out for independent complex sentence type, and random practice is the random white silk for including entire learning Content
It practises.Study schedule shows for the visualization for the study schedule that learner provides complex sentence type and its two-level grammar point.Three big moulds
Block constructs jointly to be relied on complex sentence knowledge base, by recommended models recommends appropriate to learner according to user model
It practises resource and exercise, study-leading person learns and contact, and study schedule is vividly showed to the external Chinese of learner
Grammer adaptive and learning system.
Wherein, user model is the core of adaptive and learning system, reflects learner and system interaction, performance row
For with learning records etc. are subjective and objective information.It is divided into three parts: first is that learner's essential information model, refers to learner's
Account number cipher information needed for the attribute information for being not easy to change and login system of the normalities such as name, gender, country origin, age;
Second is that learner's learning behavior model, refer to learner using a series of operation generated in systematic procedure and operation when
Intermediate node, such as: login time, exit time, learning behavior type etc., in order to learner behavior carry out deeper into examine
It examines, the data such as the time of answering generated during practice to learner, object of answering, result of answering also will record, and make
For the foundation of learner's Grasping level judgement;Third is that learner's Grasping level model, i.e., learner is to complex sentence and its two-level grammar
The grasp situation of point.
1) learner first logs into system and needs to carry out account, and registration user name, country origin, the age, HSK (examine by Chinese proficiency
Examination) etc. essential informations.
2) the HSK grade filled according to user's registration, system determine whether to need preceding survey, and HSK grade, which is greater than 3 grades, then to be needed
Want preceding survey, to judge whether the currently practical grasp level of learner is consistent with grade, grade be less than or equal to 3 grades do not need then before
It surveys.
As shown in figure 4, to carry out the flow chart of the preceding interactive learning methods surveyed in the present embodiment:
Step S102, the basic information data for obtaining user determines preceding survey content, and judges that learner's opriginal language is horizontal,
Wherein the essential information data of the user refer to: current language hierarchy level.
Step S103 determines initial learning model according to the language initial level of user;
Step S105 updates the initial learning model, Yong Hugen using adaptive algorithm according to current language level
Language learning is carried out according to updated learning model.
3) as shown in figure 5, if learner surveys before needing, the system of the present embodiment is surveyed before being provided according to different HSK grades
Examination question will do it degradation and resurvey to its preset user model according to the adjustment of answer situation if completeness is lower than 60%,
It is retested according to the HSK grade preset model of low level-one, until being equal to 3 grades by testing or testing grade, is not necessarily to
Test.It, can be default to it for corresponding knowledge point according to the situation of answering of preceding test question if completeness is lower than 80%
Be adjusted with user model after, complete the initial setting up of user model.
4) as learner does not need to test or it is non-first log into, system can enter homepage, present respectively study module,
Exercise module and progress display module, study project is voluntarily selected by learner.
5) wherein study module can be horizontal according to the grasp of active user, automatic horizontal lower to recommending to grasp in the stage
5 knowledge points recommend learner learn.
It is illustrated in figure 6 in the embodiment of the present invention, about the adaptive learning flow chart of learning model, logging in system by user
Afterwards, system reads the current language acquisition level of user, selects autonomous learning or practice by user, and user selects after practicing, and is
It unites and recommends learning Content to user;
User select practice when, system to user recommend practice topic, user complete practice topic simultaneously submit, system according to
For the practice result that user submits to user feedback answer as a result, level is grasped in simultaneity factor amendment, update user currently grasps water
It is flat, it is horizontal with the grasp for the user that timely updates, provide a user the learning Content for more meeting user language level or practice topic.
6) after entering study module, each corresponding knowledge point can be annotated by Chinese and English, and practical corpus example, picture is said
The bright form with video explanation carries out autonomous learning, respects the learning style of learner individual.
7) for temporarily failing to understand or grasp not firm enough knowledge point, learner can pass through the shape in addition new word library
Formula is safeguarded that a knowledge point is paid close attention in selection.
8) in system homepage, learner, which also can choose, directly to practice, and exercise module provides random practice and specially
Item practice both of which.
9) wherein random practice refers to, the overall condition that system currently grasps knowledge point for learner, mainly for
Horizontal lower knowledge point is grasped in learner's stage, is extracted 10 topics from test item bank at random and is practiced for learner.
10) wherein specific exercises refer to, learner can voluntarily select to want the special knowledge point of practice, for the knowledge
Point grasps level in conjunction with current learner, extracts topic relevant with knowledge point and practices for learner.
11) each answer situation of learner can all be recorded, and by the associated knowledge point of topic, topic is returned
It answers situation, situation directly is grasped to the knowledge point of learner and is adjusted, and as carrying out study recommendation next time and practice topic
The reference data of extraction.
12) intuitively to understand study schedule convenient for learner, system also by visual form, show respectively it is whole and
The grasp progress of different knowledge points fills a vacancy for learner's progress leakage detection and provides guide.
In the embodiment of the present invention, the learning model (initial learning model and current learning model) of user includes: study
The essential information model of person, the learning behavior model of learner and learner's Grasping level model.
The essential information model of learner is metastable information, including learner's essential information (user name, password,
Name), demographic's (gender, country origin, age) and current Chinese proficiency information (HSK grade), these information are in learner
Voluntarily typing is needed when registering login system for the first time.Wherein learner's essential information is letter required for learner's login system
Breath.In system use process, with the accumulation of data, demographic equally has important value to data analysis, such as
It checks country origin difference, understands different characteristics of the gender differences in language learning, the particularity etc. of different age group crowd is right
It has great significance in preferably sophisticated systems.Meanwhile the HSK grade of learner's input also can be that system carries out learner
Preceding survey and the preset foundation of user model, learner's essential information model table structure are as shown in table 1.
1 learner's essential information table structure of table
The learning behavior model of learner is mainly the relevant operation that recording learning person is carried out when using learning system
Data information wherein login time and exit time, can be with such as table 2, including login time, exit time and action type
The allocation of study time and study duration for reflecting learner, reflect the learning style of learner and the enthusiasm of study.
Action type is divided into study and practice two types, and the record of learning behavior can observe effectively, the study of analytic learning person
When feature, such as learner's habit are learning, and when are practiced, can also be certain in conjunction with the feedback of practice
Reflect the learning effect of learner in degree.
2 learner's learning behavior model of table
Field attribute name | Data type | It could be sky | Field meanings |
user_id | varchar | n | User name |
star time | date | n | Login time |
exit time | date | n | Exit time |
operation | varchar | n | Action type |
In addition to system logins the operation note published, in order to comprehensively hold the learning level current intelligence of learner, also
Need to record behavior of the learner in practice activity, including answer overall duration (by answer the beginning and ending time meter
Calculate), practice type (specific exercises and random practice) and topic number, user answer result and answer as a result, wherein inscribing
The grammer that mesh number is associated with corresponding topic investigates point, by that the learner can be speculated to investigation grammer the judgement of answer result
The grasp situation of point, as shown in table 3:.
3 learner's answer situation of table records table structure
Field attribute name | Data type | It could be sky | Field meanings |
user_id | varchar | n | User name |
star_time | date | n | It answers the time started |
out_time | date | n | It answers the end time |
operation | varchar | n | Practice type (special or random) |
question_id | varchar | n | Topic number |
stu_answer | varchar | n | User answers |
result | varchar | n | Answer result |
Learner's Grasping level model observes itself study schedule and existing deficiency in order to facilitate learner at any time,
It needs currently to have a feedback in real time to the Grasping level of each grammer point to learner, while as a learning system,
It is intended to be continuously improved the Grasping level of learner, therefore, is different from general test macro, the learner of adaptive and learning system
The level of understanding be that record is lasted based on the situation of answering to learner's routine, is counted in continuous variation
It can preferably reflect the current Grasping level of learner.
On the basis of learning behavior record, to the study schedule of learner also using the form of dynamically recording, with
The dynamic change for sufficiently reflecting grasp to grammer point of the learner in learning process, is conducive to preferably analytic learning person
Learning ability, including account, grammer point (complex sentence type, two-level grammar point), timing node information, grasp horizontal, Yi Jixue
Habit person's label.Wherein, learner's label refers to that learner can voluntarily add in the learning process of knowledge point and has grasped and do not slapped
The label held knowledge point list and does not grasp knowledge point list to generate to grasp, learner is facilitated voluntarily to consult, but not
Judge that learner grasps horizontal foundation to the knowledge point as system.Based on the data that these are lasted, by timing node and
The horizontal variation of grasp can study to the analysis that study situation of the learner to some knowledge point is lasted, to learner
Ability is in depth judged that specific structure is as shown in table 4:
4 learner's study schedule table structure of table
Field attribute name | Data type | It could be sky | Field meanings |
user_id | varchar | n | User name |
type | varchar | n | Complex sentence type |
grammar point | varchar | n | Grammer point |
level | varchar | n | It grasps horizontal |
time | date | n | Time |
mark | varchar | y | Learner's label |
Chinese studying systems disclosed by the embodiments of the present invention are the answer situations by learner come the grasp to learner
Degree Model carries out dynamic adjustment, i.e., the situation of answering of each learner can be reflected in the variation of the level of understanding.Pass through
Topic answers situation to reflect the Grasping level of learner, it is also contemplated that the factor of some contingency that may be present
Influence, for example the random selection or the selection made in the case where being not to fully understand of learner of learner also have
It may be correct selection, especially at the initial stage of the acquisition of knowledge, since basis is not sturdy, even answering correctly can not be complete
Show that learner has grasped entirely.For this case, the system of the present embodiment passes through to correct and mistake setting of answering of answering
Corresponding parameter controls to adjust the mode of Grasping level variation, grasps to corresponding knowledge point setting learner when answer is correct
The promotion of degree also can be weighted reduction in learner's answer mistake with Grasping level of the learner to the knowledge point, with
The factor of contingency is avoided to influence judgement of the system to learner's Grasping level, it is right on the basis of completing a certain number of topics
The Grasping level of learner has more accurate judgement, practices learner adequately.
This system is provided with the initial parameter that following parameter is adjusted as user model for different types of topic types,
During actual use, parameter can be adjusted in conjunction with actual conditions.It is as shown in table 5:
5 user model adjusting parameter of table
Type | Correct increment | Promote the upper limit |
Memorize class | 10%* degree-of-difficulty factor | 20% |
Understand class | 13%* degree-of-difficulty factor | 50% |
Using class | 15%* degree-of-difficulty factor | 100% |
Type | Mistake decrement | Reduce lower limit |
Memorize class | 10%* degree-of-difficulty factor * 1.2 | 0% |
Understand class | 13%* degree-of-difficulty factor * 1.2 | 0% |
Using class | 15%* degree-of-difficulty factor * 1.2 | 0% |
The difference of memorize, understanding and application three classes topic type due to investigating target and itself difficulty, answers correctly to learner
The promotion weight of Grasping level be also it is different, what the topic of memorize class was only investigated is memory of the learner to grammer point,
Therefore weight is minimum, while be in the shallow-layer in instructional objective in view of memorize class topic, to being based on memorize class topic to grasp
The promotion of degree is provided with the upper limit 20%, i.e., the contribution maximum value for answering questions the level of understanding that learner passes through memorize class topic is
20%;Relative to memorize class topic, understand that class topic reflects higher Grasping level, therefore weight is higher, based on understanding class
The Grasping level upper limit that topic is promoted is also set to 50%;Application level is this system to final teaching mesh required by learner
Mark, therefore the instructional objective for also meaning that using class topic and having reached system can be smoothly completed, therefore, using class topic
The contribution answered i.e. maximum, and the upper limit is promoted based on the Grasping level that application class topic obtains and can achieve
100%, i.e., it grasps completely.
In addition to being directed to setting of the different type topic to the Grasping level upper limit, tribute of the topic of different difficulty to Grasping level
Offer also not identical, promotion can be had to the Grasping level of learner by answering correctly, need setting weight bigger if mistake of answering
Deduct parameter, on topic types, the present embodiment respectively to memorize class, understand class and application class be provided with 10%, 20% and
30% adjusting parameter;On item difficulty, (degree-of-difficulty factor=1+ is difficult for the grade of difficulty setting degree-of-difficulty factor weight based on topic
Spend grade × 0.1);Answering in result, for answer mistake in the case of, provided with 1.2 times of decrement weight, that is, mistake of answering
In the case where, deduct the Grasping level of bigger specific gravity.Such as understand the topic that difficulty is 3 in class, degree-of-difficulty factor is 1+3 × 0.1
=1.3, if it is correct to answer, Grasping level lifting values are that 13% × 1.3=16.9% is grasped if answering mistake
Degree can then reduce by 13% × 1.3 × 1.2=16.9%=20.28%, by this dynamic adjustment mechanism, to a certain extent
Reduce the influence that accidentalia judges learner's Grasping level, avoids correctly answering questions grasp journey caused by non-accidental cause
Degree generates large effect, is conducive to the real conditions for preferably reflecting learner, and guarantee practice appropriate.
In the prior art, for different teaching purpose and teaching demand, there is diversified Model of Network Teaching
And teaching resource.But existing language learning research is concentrated mainly on conventional Multi Media technology auxiliary Chinese teaching and teaching money
In terms of the construction in source library, the different characteristics of learner could not be sufficiently looked after.Interactive learning methods and device of the invention, sufficiently
The learning motivation for considering learner fully demonstrates learner center, guarantees the language communication for expeditiously improving learner
Ability effectively improves the learning efficiency of learner, rather than simply builds rambling educational resource one by one.Right
The demand of learner makes corresponding teaching resource on the basis of fully understanding, and is provided these using adaptive learning technology
Source carries out significantly more efficient utilization, just teaching resource can be made to be used in the suitable time by suitable learner, make teaching resource
Maximum value is played, to realize more effectively study.The meaning of adaptive learning, which is not only in that, to be formulated for learner
It is more in line with the study route of personal touch, while being that cultivating learner takes learning strategy appropriate and form good
It is breezy used, facilitate the efficient study and life-long education of learner.
It should be understood by those skilled in the art that, the embodiment of the present invention can provide as method, system or computer program
Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the present invention
Apply the form of example.Moreover, it wherein includes the computer of computer usable program code that the present invention, which can be used in one or more,
The computer program implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) produces
The form of product.
The present invention be referring to according to the method for the embodiment of the present invention, the process of equipment (system) and computer program product
Figure and/or block diagram describe.It should be understood that every one stream in flowchart and/or the block diagram can be realized by computer program instructions
The combination of process and/or box in journey and/or box and flowchart and/or the block diagram.It can provide these computer programs
Instruct the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce
A raw machine, so that being generated by the instruction that computer or the processor of other programmable data processing devices execute for real
The device for the function of being specified in present one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates,
Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or
The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one
The step of function of being specified in a box or multiple boxes.
Specific embodiment is applied in the present invention, and principle and implementation of the present invention are described, above embodiments
Explanation be merely used to help understand method and its core concept of the invention;At the same time, for those skilled in the art,
According to the thought of the present invention, there will be changes in the specific implementation manner and application range, in conclusion in this specification
Appearance should not be construed as limiting the invention.
Claims (10)
1. a kind of interactive learning methods, which is characterized in that the method includes:
The language learning interaction data for obtaining user determines the language proficiency of user;Wherein, the language proficiency number of the user
According to language initial level, the current language level for including: user;
Initial learning model is determined according to the language initial level of the user;
The initial learning model is updated using adaptive algorithm according to the current language level, after user is according to update
Learning model carry out language learning.
2. interactive learning methods as described in claim 1, which is characterized in that the language learning interaction data includes: use
Essential information data, the learning behavior data of user of family input.
3. interactive learning methods as claimed in claim 2, which is characterized in that the language learning interaction number of the acquisition user
According to determine user language proficiency include:
The language initial level of user is determined according to the essential information data that user inputs;Wherein, the essential information data
It include: age of user, country origin and current language hierarchy level;
Current language level is determined according to the learning behavior data for obtaining user;Wherein the learning behavior data include: user
Learning Content data, test data and practice data.
4. interactive learning methods as claimed in claim 3, which is characterized in that the language initial water according to the user
It is flat to determine that initial learning model includes:
The learning Content of corresponding user's initial level is selected from preset teaching database according to the language initial level of user
Data, test data and practice data are as initial learning model.
5. interactive learning methods as claimed in claim 4, which is characterized in that described according to the horizontal benefit of the current language
Updating the initial learning model with adaptive algorithm includes:
Corresponding weighted value is respectively set to the learning Content data of user, test data, practice data;
The current language of user is determined according to the learning Content data of user, test data, practice data and corresponding weighted value
It is horizontal;
The learning Content data of corresponding user's present level are selected from preset teaching database according to current language proficiency,
Test data and practice data are as current learning model.
6. a kind of language learning device, which is characterized in that the device includes:
Interaction data processing module, the language learning interaction data for obtaining user determine the language proficiency of user;Wherein, institute
The language proficiency of the user stated includes: the language initial level of user, current language level;
Model building module, for determining initial learning model according to the language initial level of the user;
Model modification module, for updating the initial study mould using adaptive algorithm according to the current language level
Type, user carry out language learning according to updated learning model.
7. language learning device as claimed in claim 6, which is characterized in that the language learning interaction data includes: use
Essential information data, the learning behavior data of user of family input.
8. language learning device as claimed in claim 7, which is characterized in that the interaction data processing module includes:
Basic processing unit determines the language initial level of user according to the essential information data that user inputs;Wherein, institute
The essential information data stated include: age of user, country origin and current language hierarchy level;
Learning data processing unit determines current language level according to the learning behavior data for obtaining user;The wherein study
Behavioral data includes: the practice data of user, learning Content data.
9. language learning device as claimed in claim 6, which is characterized in that the model building module, further basis
The language initial level of user selects the teaching data and practice number of corresponding user's initial level from preset teaching database
According to as initial learning model.
10. language learning device as claimed in claim 6, which is characterized in that the model modification module includes:
Corresponding power is respectively set for learning Content data, test data, the practice data to user in weight value setting unit
Weight values;
Language proficiency determination unit, it is true according to the learning Content data of user, test data, practice data and corresponding weighted value
The current language for determining user is horizontal;
Model modification unit, for selecting the corresponding current water of user from preset teaching database according to current language proficiency
Flat learning Content data, test data and practice data update initial learning model as current learning model.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201811234259.7A CN109254991A (en) | 2018-10-23 | 2018-10-23 | A kind of interactive learning methods and device |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201811234259.7A CN109254991A (en) | 2018-10-23 | 2018-10-23 | A kind of interactive learning methods and device |
Publications (1)
Publication Number | Publication Date |
---|---|
CN109254991A true CN109254991A (en) | 2019-01-22 |
Family
ID=65045720
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201811234259.7A Withdrawn CN109254991A (en) | 2018-10-23 | 2018-10-23 | A kind of interactive learning methods and device |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN109254991A (en) |
Cited By (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110428803A (en) * | 2019-07-22 | 2019-11-08 | 北京语言大学 | A kind of recognition methods of speaker country origin and system based on pronunciation attribute |
CN111898020A (en) * | 2020-06-18 | 2020-11-06 | 济南浪潮高新科技投资发展有限公司 | Knowledge learning system recommendation method, device and medium based on BERT and LSTM |
CN111932415A (en) * | 2020-08-10 | 2020-11-13 | 广东讯飞启明科技发展有限公司 | Method and device for language self-adaptive hierarchical learning |
CN111984131A (en) * | 2020-07-07 | 2020-11-24 | 北京语言大学 | Method and system for inputting information based on dynamic weight |
CN112053020A (en) * | 2019-06-06 | 2020-12-08 | 兰贵曜威公司 | Method, system and equipment for providing man-machine interactive auxiliary language learning |
CN112860983A (en) * | 2019-11-27 | 2021-05-28 | 上海流利说信息技术有限公司 | Learning content pushing method, system, equipment and readable storage medium |
CN113095071A (en) * | 2021-04-28 | 2021-07-09 | 杭州菲助科技有限公司 | System and method for marking English video or text difficulty pairs to domestic grades |
CN113205717A (en) * | 2021-05-07 | 2021-08-03 | 江苏熙枫教育科技有限公司 | Deep learning-based oral English training method |
CN113506481A (en) * | 2021-07-14 | 2021-10-15 | 郑州轻工业大学 | Computer teaching system based on cloud computing and big data |
CN113806515A (en) * | 2021-09-20 | 2021-12-17 | 曹庆恒 | Language teaching system and use method thereof, and computer readable storage medium |
-
2018
- 2018-10-23 CN CN201811234259.7A patent/CN109254991A/en not_active Withdrawn
Cited By (13)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112053020A (en) * | 2019-06-06 | 2020-12-08 | 兰贵曜威公司 | Method, system and equipment for providing man-machine interactive auxiliary language learning |
CN110428803B (en) * | 2019-07-22 | 2020-04-28 | 北京语言大学 | Pronunciation attribute-based speaker country recognition model modeling method and system |
CN110428803A (en) * | 2019-07-22 | 2019-11-08 | 北京语言大学 | A kind of recognition methods of speaker country origin and system based on pronunciation attribute |
CN112860983B (en) * | 2019-11-27 | 2023-11-24 | 上海流利说信息技术有限公司 | Method, system, equipment and readable storage medium for pushing learning content |
CN112860983A (en) * | 2019-11-27 | 2021-05-28 | 上海流利说信息技术有限公司 | Learning content pushing method, system, equipment and readable storage medium |
CN111898020A (en) * | 2020-06-18 | 2020-11-06 | 济南浪潮高新科技投资发展有限公司 | Knowledge learning system recommendation method, device and medium based on BERT and LSTM |
CN111984131A (en) * | 2020-07-07 | 2020-11-24 | 北京语言大学 | Method and system for inputting information based on dynamic weight |
CN111984131B (en) * | 2020-07-07 | 2021-05-14 | 北京语言大学 | Method and system for inputting information based on dynamic weight |
CN111932415A (en) * | 2020-08-10 | 2020-11-13 | 广东讯飞启明科技发展有限公司 | Method and device for language self-adaptive hierarchical learning |
CN113095071A (en) * | 2021-04-28 | 2021-07-09 | 杭州菲助科技有限公司 | System and method for marking English video or text difficulty pairs to domestic grades |
CN113205717A (en) * | 2021-05-07 | 2021-08-03 | 江苏熙枫教育科技有限公司 | Deep learning-based oral English training method |
CN113506481A (en) * | 2021-07-14 | 2021-10-15 | 郑州轻工业大学 | Computer teaching system based on cloud computing and big data |
CN113806515A (en) * | 2021-09-20 | 2021-12-17 | 曹庆恒 | Language teaching system and use method thereof, and computer readable storage medium |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN109254991A (en) | A kind of interactive learning methods and device | |
CN101872557A (en) | Progressive learning management method and progressive learning system | |
US20140335498A1 (en) | Generating, assigning, and evaluating different versions of a test | |
Ahmed et al. | A model of adaptive e-Learning in an ODL environment | |
US20120077180A1 (en) | Method and system for knowledge representation and processing using a structured visual idea map | |
Bassi et al. | Software agents in large scale open e-learning: A critical component for the future of massive online courses (MOOCs) | |
KR20150116662A (en) | Toeic-Navigation | |
Alghamdi et al. | Assessing the impact of web-based technology on learning styles in education | |
Yu et al. | Designing an artificial intelligence platform to assist undergraduate in art and design to develop a personal learning plans | |
Fang et al. | External and Internal Predictors of Student Satisfaction with Online Learning Achievement. | |
Svihla et al. | Distributed Revisiting: An Analytic for Retention of Coherent Science Learning. | |
Zhou | Construction of self-learning platform for English reading based on artificial intelligence | |
Hnida et al. | Overview of CALEP: A competency based learning path generation system | |
Kostolányová et al. | Adaptive e-learning and its evaluation | |
Jayasiriwardene et al. | A knowledge-based adaptive algorithm to recommend interactive learning assessments | |
Zhu | Research on computer assisted English teaching in foreign language teaching in colleges | |
Xu et al. | Teacher Video Learning Research Overview and Insights | |
Serral et al. | Conceptualizing the Domain of Automated Feedback for Learners. | |
Slesarenko et al. | Blended Learning in Training Courses for University Content Teachers | |
Choi et al. | The Effects of Task Selection Approaches to Emphasis Manipulation on Cognitive Load and Knowledge Transfer. | |
Daulay | The Development Of Computer-Based Learning Media At A Vocational High School | |
Xiong et al. | Exploration and research on web programming course in higher vocational college | |
Musumba et al. | Towards a Personalized Adaptive Remedial e-Learning Model | |
Behal et al. | Analysis of Barriers in Conduct of Lab Based Courses in Remote Teaching Learning Paradigm | |
Ding | Development of computer-aided English listening system based on BS architecture |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
WW01 | Invention patent application withdrawn after publication |
Application publication date: 20190122 |
|
WW01 | Invention patent application withdrawn after publication |